scikit-learnRegression
Machine LearningIntermediate
Machine Learning A–Z with scikit-learn
A rigorous yet practical tour of supervised and unsupervised learning, model evaluation and ML workflows.
52 hrs6 modules17.3K learners
₹5,499₹11,999
Machine Learning AdvancedNew
Deep Learning & Neural Networks with PyTorch
Build CNNs, transformers and generative models from first principles.
9.1K learners English Updated October 2026
Taught by Rohan Kapoor, Machine Learning Researcher
What you'll learn
- Implement neural networks and backprop from scratch
- Train CNNs for image classification and detection
- Understand attention and transformer architectures
- Fine-tune pretrained models for your own data
- Apply regularisation, optimisation and debugging techniques
About this course
An advanced deep-dive for learners with ML basics. You'll implement neural networks from scratch, then scale up to CNNs, sequence models and transformers using PyTorch with best-practice training loops.
- PyTorch
- CNN
- Transformers
- NLP
- Computer Vision
Syllabus overview
5 modules · 48 hrs total. Full lessons are delivered inside the Hyskilled app.
Perceptrons, backprop and optimisers.
Requirements
- Solid Python
- Basic ML knowledge
- Comfort with linear algebra basics
Who this is for
- ML practitioners
- Engineers targeting AI research or applied-AI roles
Your instructor
RK
Rohan Kapoor
Machine Learning Researcher
- 15,700 learners
- 2 courses
Rohan bridges research and practice, with experience in computer vision and NLP. He explains the intuition first, then the math, then the code.
Frequently asked questions
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